Malware attacks on systems throughout the world are prevalent and dangerous. Extortion, theft, and blackmail plague users of infected systems. As malware attacks become more sophisticated, increasingly complex techniques are being used to thwart the attackers. Machine learning techniques are one way to train systems to prevent malware attacks. Automated machine learning techniques have demonstrated powerfulness in malware detection. However, adversary malware attackers often attempt to poison malware training data, tricking the machine learning systems to produce incorrect models. This results in a degraded classification accuracy or high false positives, affecting the effectiveness of the machine learning systems.